Prediction of Squat for Underkeel Clearance

Briggs, Michael J.; Vantorre, Marc; Uliczka, Klemens; Debaillon, Pierre

doi:10.1142/9789813204027_0036

Cited by 4 publications

(3 citation statements)

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“…Predictions of ship squat are mainly govern by channel and ship dimensions, and the relative speed of the ship in water (Barrass and Derrett 2012). Along an estuarine channel, cross-sections are generally variable and can be characterized as (i) unrestricted channels, (ii) restricted channels or (iii) canals (Briggs et al 2009). These channel types differ according to their proximity to the channel margins and bottom position, width, and bank slopes.…”

Section: Prediction Of Squat and Management Of Underkeel Clearancementioning

confidence: 99%

“…These channel types differ according to their proximity to the channel margins and bottom position, width, and bank slopes. In most cases, the ship squat is expected to vary as the square of the relative speed of the ship in water (Briggs et al 2009) and becomes significant when the ratio between the water depth to the ship draft is < 1.5 − 2.…”

Section: Prediction Of Squat and Management Of Underkeel Clearancementioning

confidence: 99%

“…In NAVIRE, the squat is determined by empirical formulas recommended by the Permanent International Association of Navigation Congresses (PIANC) during the designing of a navigation channel (Briggs et al 2009). These formulas can be divided into two groups, according to the phases of the design process, namely the concept design phase and the detailed design phase.…”

Section: Prediction Of Squat and Management Of Underkeel Clearancementioning

confidence: 99%

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NavTEL: Open-Source Decision Support Tool for Ship Routing and Underkeel Clearance Management in Estuarine Channels

Orseau

Huybrechts

Tassi

et al. 2021

J. Waterway, Port, Coastal, Ocean Eng.

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NavTEL is a new decision support tool for the short-term (36 hours) planning of ship routes and the management of underkeel clearance in estuarine navigation channels.NavTEL used a deterministic method and is coupled with the TELEMAC-MASCARET system for numerical modeling of hydrodynamic and sediment transport in the estuary with a two-dimensional approach. In its present version, NavTEL allows to (i) prepare and launch daily simulations automatically, and (ii) to post-process simulation outputs to find the safest ship route and to predict underkeel clearances at specified locations. Because of the reliability of the results lies on the accuracy of water level predictions, numerical simulations were performed with measured river discharges, storm surge forecasts and time-varying friction coefficients for bed roughness. Even though NavTEL was initially developed for the Atlantic Port of Bordeaux located in the Gironde Estuary, its kernel has a modular structure allowing to adjust the tool to different port configurations and types of water bodies. Finally, examples of graphical outputs and reports generated by NavTEL are shown for an application of a container ship coming into the port of Bordeaux.

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Section: Prediction Of Squat and Management Of Underkeel Clearancementioning

confidence: 99%

Section: Prediction Of Squat and Management Of Underkeel Clearancementioning

confidence: 99%

Section: Prediction Of Squat and Management Of Underkeel Clearancementioning

confidence: 99%

See 1 more Smart Citation

NavTEL: Open-Source Decision Support Tool for Ship Routing and Underkeel Clearance Management in Estuarine Channels

Orseau

Huybrechts

Tassi

et al. 2021

J. Waterway, Port, Coastal, Ocean Eng.

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Predictive Model for Operational Drafts and Cargo Capacities of River Convoys Based on Machine Learning

de Figueiredo,

Campos Filho,

Blanco

et al. 2023

Preprint

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Machine Learning for the Sustainable Management of Depth Prediction and Load Optimization in River Convoys: An Amazon Basin Case Study

Campos Filho,

Figueiredo,

Blanco

et al. 2024

Sustainability

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The seasonal fluctuation of river depths is a critical factor in designing cargo capacity for river convoys and logistics processes used for grain transportation in northern Brazil. Water level variations directly impact the load capacities of pusher convoys navigating the Amazon rivers. This paper presents a machine learning model based on a multilayer perceptron artificial neural network developed with the aim of estimating the cargo capacities of river convoys one year in advance, which is essential for determining load capacities during dry periods. The prediction model was applied to the Tapajós River in the Amazon Basin, Brazil, where grain transportation is significant and relies on inland waterways. Navigability conditions were evaluated in terms of depth and geometric parameters. The results of this case study were satisfactory, validating the computational tool and enabling the assessment of capacity losses during dry periods and the identification of navigation bottlenecks. The main contributions of this work include optimizing river logistics, reducing costs, minimizing environmental impacts, and promoting the sustainable management of water resources in the Amazon. Conclusions drawn from the study indicate that the developed model is highly effective, with an R2 of 0.954 and RMSE of 0.095, demonstrating its potential to significantly enhance river convoy operations and support sustainable development in the region.

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Prediction of Squat for Underkeel Clearance

Cited by 4 publications

References 0 publications

NavTEL: Open-Source Decision Support Tool for Ship Routing and Underkeel Clearance Management in Estuarine Channels

NavTEL: Open-Source Decision Support Tool for Ship Routing and Underkeel Clearance Management in Estuarine Channels

Predictive Model for Operational Drafts and Cargo Capacities of River Convoys Based on Machine Learning

Machine Learning for the Sustainable Management of Depth Prediction and Load Optimization in River Convoys: An Amazon Basin Case Study

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